Web Distributed Computing Systems Implementation and Modeling

Web Distributed Computing Systems Implementation and Modeling

Fabio Boldrin, Chiara Taddia, Gianluca Mazzini
Copyright: © 2010 |Volume: 1 |Issue: 1 |Pages: 17
ISSN: 1947-9220|EISSN: 1947-9239|ISSN: 1947-9220|EISBN13: 9781616929671|EISSN: 1947-9239|DOI: 10.4018/jaras.2010071705
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MLA

Boldrin, Fabio, et al. "Web Distributed Computing Systems Implementation and Modeling." IJARAS vol.1, no.1 2010: pp.75-91. http://doi.org/10.4018/jaras.2010071705

APA

Boldrin, F., Taddia, C., & Mazzini, G. (2010). Web Distributed Computing Systems Implementation and Modeling. International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), 1(1), 75-91. http://doi.org/10.4018/jaras.2010071705

Chicago

Boldrin, Fabio, Chiara Taddia, and Gianluca Mazzini. "Web Distributed Computing Systems Implementation and Modeling," International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS) 1, no.1: 75-91. http://doi.org/10.4018/jaras.2010071705

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Abstract

This article proposes a new approach for distributed computing. The main novelty consists in the exploitation of Web browsers as clients, thanks to the availability of JavaScript, AJAX and Flex. The described solution has two main advantages: it is client-free, so no additional programs have to be installed to perform the computation, and it requires low CPU usage, so client-side computation is no invasive for users. The solution is developed using both AJAX and Adobe®Flex® technologies embedding a pseudo-client into a Web page that hosts the computation. While users browse the hosting Web page, computation takes place resolving single sub-problems and sending the solution to the server-side part of the system. Our client-free solution is an example of high resilient and auto-administrated system that is able to organize the scheduling of the processes and the error management in an autonomic manner. A mathematical model has been developed over this solution. The main goals of the model are to describe and classify different categories of problems on the basis of the feasibility and to find the limits in the dimensioning of the scheduling systems to have convenience in the use of this approach. The new architecture has been tested through different performance metrics by implementing two examples of distributed computing, the cracking of an RSA cryptosystem through the factorization of the public key and the correlation index between samples in genetic data sets. Results have shown good feasibility of this approach both in a closed environment and also in an Internet environment, in a typical real situation.

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